Abstract
AI applications are increasing in the field of education, from laboratory set-ups to contemporary and complex learning systems. A great example of such systems is AI-enabled adaptive learning systems (AI-ALS) that promote adaptive learning. Despite its promised potential, there are challenges such as design issues, highly complex models, and lack of evidence-based guidelines and design principles that hinder the large-scale adoption and implementation of AI-ALS. The goal of this paper thus is to establish a set of empirically grounded design principles (DPs) of AI-ALS, that would serve well in a university context. 22 interviews were con-ducted with experts knowledgeable about the design and development of AI-ALS. Several rounds of coding and deep analysis of the expert interviews revealed features and functionalities of AI-ALS; purposes for designing and using AI-ALS; and recommended improvements for AI-ALS as requirements. These requirements were translated to 13 preliminary DPs. The findings of this study serve as a guide on how to better design AI-ALS, that will improve the learning experiences of students.
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Kabudi, T., Pappas, I.O., Olsen, D.H. (2022). Deriving Design Principles for AI-Adaptive Learning Systems: Findings from Interviews with Experts. In: Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y.K., Mäntymäki, M., Pappas, I.O. (eds) The Role of Digital Technologies in Shaping the Post-Pandemic World. I3E 2022. Lecture Notes in Computer Science, vol 13454. Springer, Cham. https://doi.org/10.1007/978-3-031-15342-6_7
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DOI: https://doi.org/10.1007/978-3-031-15342-6_7
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